一、目的
使用CNN網絡對Mnist進行分類。
二、程式設計
我們使用pytorch架構。
搭建兩個卷積層、兩個池化層、一個全連接配接層來實作。
![](https://img.laitimes.com/img/9ZDMuAjOiMmIsIjOiQnIsIiclRnblN2XjlGcjAzNfRHLGZkRGZkRfJ3bs92YsYTMfVmepNHL4VkeOVTWE90dRpHW4Z0MMBjVtJWd0ckW65UbM5WOHJWa5kHT20ESjBjUIF2X0hXZ0xCMx81dvRWYoNHLrdEZwZ1Rh5WNXp1bwNjW1ZUba9VZwlHdssmch1mclRXY39CXldWYtlWPzNXZj9mcw1ycz9WL49zZuBnL2MTOyATMxYTM0IzMwEjMwIzLc52YucWbp5GZzNmLn9Gbi1yZtl2Lc9CX6MHc0RHaiojIsJye.png)
1、每一個卷積核它的通道數量要求和輸入通道數量是一樣的。這種卷積核的總數有多少個和你輸出通道的數量是一樣的。
2、卷積(convolution)後,C(Channels)變,W(width)和H(Height)可變可不變,取決于是否padding。subsampling(或pooling)後,C不變,W和H變。
3、卷積層:保留圖像的空間資訊。
4、卷積層要求輸入輸出是四維張量(B,C,W,H),全連接配接層的輸入與輸出都是二維張量(B,Input_feature)。
5、卷積(線性變換),激活函數(非線性變換),池化;這個過程若幹次後,view打平,進入全連接配接層~
import torch.nn.functional as F
import torch
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
# 準備資料集
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_data = datasets.MNIST(root='./dataset/mnist', train=True, transform=transform, download=True)
train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=0)
test_data = datasets.MNIST(root='./dataset/mnist', train=False, transform=transform, download=True)
test_loader = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True, num_workers=0)
# 搭建網絡模型
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(in_channels=10, out_channels=20, kernel_size=5)
self.pooling = torch.nn.MaxPool2d(kernel_size=2)
self.fc = torch.nn.Linear(320, 10)
def forward(self, x):
batch_size = x.shape[0]
# 卷積後池化
x = self.pooling(F.relu(self.conv1(x)))
x = self.pooling(F.relu(self.conv2(x)))
x = x.view(batch_size, -1)
x = self.fc(x)
return x
model = Net()
# 建構損失函數和優化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), momentum=0.5, lr=0.01)
# 訓練
def train(epoch):
train_loss = 0.
for batch_idx, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
if batch_idx % 300 == 299:
print('[%d, %d],loss=%.3f' % (epoch+1, batch_idx+1, train_loss/300))
train_loss = 0.
def test():
correct = 0.
total = 0
with torch.no_grad():
for batch_idx, data in enumerate(test_loader, 0):
inputs, labels = data
outputs = model(inputs)
_, predict = torch.max(outputs, dim=1)
total += labels.size(0)
correct += (predict == labels).sum().item()
print('accuracy on the test set: %d%%' % (100*correct/total))
if __name__ == "__main__":
for epoch in range(10):
train(epoch)
test()
損失下降如下
[1, 300],loss=0.606
[1, 600],loss=0.192
[1, 900],loss=0.139
accuracy on the test set: 96%
[2, 300],loss=0.110
[2, 600],loss=0.099
[2, 900],loss=0.093
accuracy on the test set: 97%
[3, 300],loss=0.082
[3, 600],loss=0.076
[3, 900],loss=0.071
accuracy on the test set: 97%
[4, 300],loss=0.067
[4, 600],loss=0.063
[4, 900],loss=0.064
accuracy on the test set: 98%
[5, 300],loss=0.060
[5, 600],loss=0.055
[5, 900],loss=0.056
accuracy on the test set: 98%
[6, 300],loss=0.047
[6, 600],loss=0.046
[6, 900],loss=0.060
accuracy on the test set: 98%
[7, 300],loss=0.048
[7, 600],loss=0.046
[7, 900],loss=0.045
accuracy on the test set: 98%
[8, 300],loss=0.042
[8, 600],loss=0.045
[8, 900],loss=0.043
accuracy on the test set: 98%
[9, 300],loss=0.039
[9, 600],loss=0.038
[9, 900],loss=0.045
accuracy on the test set: 98%
[10, 300],loss=0.037
[10, 600],loss=0.038
[10, 900],loss=0.039
accuracy on the test set: 98%
在之前的單純全連接配接神經網絡上生生的将錯誤率降低了1/3.
下面是課後作業:
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(in_channels=10, out_channels=20, kernel_size=3)
self.conv3 = torch.nn.Conv2d(in_channels=20, out_channels=30, kernel_size=2)
self.pooling = torch.nn.MaxPool2d(kernel_size=2)
self.l1 = torch.nn.Linear(120, 64)
self.l2 = torch.nn.Linear(64, 32)
self.l3 = torch.nn.Linear(32, 10)
def forward(self, x):
batch_size = x.shape[0]
# 卷積後池化
x = self.pooling(F.relu(self.conv1(x)))
x = self.pooling(F.relu(self.conv2(x)))
x = self.pooling(F.relu(self.conv3(x)))
x = x.view(batch_size, -1)
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = self.l3(x)
return x
這個效果可能會更好一點,我的顯示卡驅動有點問題,cpu跑太慢沒有跑。
三、參考
pytorch深度學習實踐
PyTorch 深度學習實踐 第10講